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Record W2172393095 · doi:10.3148/cjdpr-2015-038

Impact of an Optional Experiential Learning Opportunity on Student Engagement and Performance in Undergraduate Nutrition Courses

2015· article· en· W2172393095 on OpenAlexaffvenue
Anne Szeto, Jess Haines, Andrea C. Buchholz

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsExperiential learningStudent engagementLikert scalePercentile rankPercentileMedical educationPsychologyThematic analysisScale (ratio)MedicineMathematics educationQualitative research

Abstract

fetched live from OpenAlex

We examined the impact of an optional experiential learning activity (ELA) on student engagement and performance in 2 undergraduate nutrition courses. The ELA involved completion of a 3-day food record, research lab tour, body composition assessment, and reflective take-home assignment. Of the 808 students in the 2 courses (1 first-year and 1 second-year course), 172 (21%) participated. Engagement was assessed by the Classroom Survey of Student Engagement (CLASSE), and performance was assessed by percentile rank on midterm and final exams. Students' perceived learning was assessed using a satisfaction survey. Paired-samples t tests examined change in CLASSE scores and percentile rank from baseline to follow-up. Frequencies and thematic analysis were used to examine responses to Likert scale and open-ended questions on the satisfaction survey, respectively. There was an 11%-22% increase (P < 0.05) in the 3 dimensions of student engagement and a greater increase in percentile rank between the midterm and final exams among participants (7.63 ± 21.9) versus nonparticipants (-1.80 ± 22.4, P < 0.001). The majority of participants indicated the ELA enhanced their interest and learning in both their personal health and the course. Findings suggest ELAs related to personal health may improve interest, engagement, and performance among undergraduate students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.212
GPT teacher head0.524
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2015
Admission routes2
Has abstractyes

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